Virtual surgical planning in endoscopic skull base surgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Skull base surgery (SBS) involves operative tasks in close proximity to critical structures in a complex three-dimensional (3D) anatomy. The aim was to investigate the value of virtual planning (VP) based on preoperative magnetic resonance imaging (MRI) for surgical planning in SBS and to compare the effects of virtual planning with 3D contours between the expert and the surgeon in training. STUDY DESIGN: Retrospective analysis. METHODS: Twelve patients with manually segmented anatomical structures based on preoperative MRI were evaluated by eight surgeons in a randomized order using a validated National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. RESULTS: Multivariate analysis revealed significant reduction of workload when using VP (P<.0001) compared to standard planning. Further, it showed that the experience level of the surgeon had a significant effect on the NASA-TLX differences (P<.05). Additional subanalysis did not reveal any significant findings regarding which type of surgeon benefits the most (P>.05). CONCLUSIONS: Preoperative anatomical segmentation with virtual surgical planning using contours in endoscopic SBS significantly reduces the workload for the expert and the surgeon in training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".